Development of a Prediction Model to Estimate the Risk of Walking Limitations in Patients with Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: Early and accurate risk prediction of walking limitations after total knee arthroplasty (TKA) is important for clinical and economic reasons. However, to our knowledge, no studies have systematically integrated multiple predictors into a single, clinically practical model. Our study aimed to develop a prediction model to estimate the risk of post-TKA walking limitations. METHODS: We performed a prospective cohort study of 1096 patients who underwent elective, primary TKA between July 2013 and September 2014. Candidate predictors included patient demographics, surgical factors, and pre- and early (1-mo) post-TKA functional measures. The outcome of interest was self-reported walking limitations at 6 months of post-TKA. We used multivariable proportional odds regression with bootstrap internal validation to develop the model. RESULTS: In all, 12% of patients reported walking limitations (maximum walk time ≤ 15 min) at 6 months postsurgery. The main predictors of increasing levels of walking limitations were preoperative walking limitations (overall p < 0.001), higher levels of body mass index [interquartile range (IQR)-OR 1.3, 95% CI 1.2-1.5], lower values of 1-month post-TKA gait speed (IQR-OR 1.9, 95% CI 1.3-2.6), the presence of contralateral knee pain (OR 1.9, 95% CI 1.2-3.0), and the use of a quadstick preoperatively (OR 3.5, 95% CI 1.7-7.3). The prediction model had an optimism-corrected concordance index of 0.71. CONCLUSION: A small but sizable proportion of patients with TKA had persistent mobility limitations. Our prediction model may help to risk-stratify patients, and external validation is required before the model can be used in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".